Learning to Select Useful

Feature Selection in Big Data by Using the enhancement of Mahalanobis–Taguchi System; Case Study, Identifiying Bad Credit clients of a Private Bank of Islamic Republic of Iran

The Mahalanobis-Taguchi System (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data. It consists of two main parts: Part 1, the selection of useful variables in order to reduce the complexity of multi-dimensional systems and part 2, diagnosis and prediction, which are used to predict the abnormal group according to the remaining us...

Nursing students’ experiences of clinical teaching: A qualitative study

Introduction: Clinical education is important part of nursing education that has a key role in the learning of nursing students. It is expected that clinical environment provides opportunities for students to gain valuable experiences. Assessment of clinical learning process based on students’ experiences can be useful in identifying important factors in students' clinical learning. Objec...

Learning to Select Useful Landmarks

Bioassay Methods useful to select the natural product cancer chemopreventive agents

Prevention of malignant disease such as cancer is obviously much more important than therapy, so primary prevention tactics should be invoked with the greatest possible effort. Because delaying a disease such as cancer is somehow equivalent to cure and it is now generally accepted that cancer can be managed through chemopreventive interventions. Since a considerable number of known cancer chemo...

Bioassay Methods useful to select the natural product cancer chemopreventive agents

Prevention of malignant disease such as cancer is obviously much more important than therapy, so primary prevention tactics should be invoked with the greatest possible effort. Because delaying a disease such as cancer is somehow equivalent to cure and it is now generally accepted that cancer can be managed through chemopreventive interventions. Since a considerable number of known cancer chemo...

Active Learning for Dependency Parsing Using Partially Annotated Sentences

Current successful probabilistic parsers require large treebanks which are difficult, time consuming, and expensive to produce. Some parts of these data do not contain any useful information for training a parser. Active learning strategies allow to select the most informative samples for annotation. Most existing active learning strategies for parsing rely on selecting uncertain sentences for ...